Rank Relationships Research Articles

From Anxiety to Aesthetics: "Rank Horror: An International Symposium".

This report on the proceedings of the "Rank Horror" symposium, convened in May 2024 to reconsider Otto Rank's life, work, and enduring contributions to psychoanalytic theory and practice, includes a brief historical survey of Rank's relationship to Sigmund Freud and other original members of the International Psychoanalytic Association's Central Committee, and an account of the symposium's wider intellectual and political context. It outlines the reasons for returning to Rank now, and a summary of the papers presented. Particular focus is afforded to Rank's 1924 book, The Trauma of Birth.

Integrated stochastic multicriteria acceptability analysis and data envelopment analysis with fixed-sum outputs: an application for evaluating participating nations in the Winter Olympics Games

Along with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations’ performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided.

Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation

Visual place recognition has gained popularity in recent years. Mainstream convolutional neural network-based methods formulate it as a ranking task and optimize it in the paradigm of deep metric learning, however, the ranking-motivated losses concern only the ranking relationship for each query image and the compactness of intra-place feature distribution is ignored. It is still challenging due to varying viewpoints, illuminations and even dynamic objects. In this paper, a novel multi-task learning framework is proposed, which combines the existing triplet ranking task and our designed binary classification task to jointly optimize the network for better generalization capability. Specifically, a binary classification network with the corresponding binary cross-entropy loss is designed in the classification task. In this way, the intra-place feature compactness and inter-place feature separability are reinforced. At testing stage, this classification network is discarded without increasing the computation cost. Furthermore, an attention module is presented to promote the network to concentrate on the salient regions by assigning different importance to each spatial position. Our method achieves the top-10 recalls of 97.27%, 94.6%, and 96.93% on Pitts250k-test, Tokyo 24/7, and TokyoTM-val datasets, respectively. Extensive experiments prove that the proposed network can learn discriminative global features with better robustness to viewpoints and environmental variations.

An Intra-Class Ranking Metric for Remote Sensing Image Retrieval

With the rapid development of internet technology in recent years, the available remote sensing image data have also been growing rapidly, which has led to an increased demand for remote sensing image retrieval. Remote sensing images contain rich visual and semantic features, and have high variability and complexity. Therefore, remote sensing image retrieval needs to fully utilize the information in the images to perform feature extraction and matching. Metric learning has been widely used in image retrieval as it can train embedding spaces with high discriminability. However, existing deep metric learning methods learn embedding spaces with high discriminability by maximizing the differences between classes, while ignoring inherent intra-class differences during the learning process. In this paper, we design a new sample generation mechanism to generate samples from positive samples that meet the boundary constraints, thus obtaining quantifiable intra-class differences from real positive samples. Based on the sample generation relationship, we use a self-supervised approach to design an intra-class ranking loss function, which improves the discriminability of the generated embedding space for samples of the same class and maintains their ranking relationship in the embedding space. Moreover, this loss function can be easily combined with existing deep metric learning methods. Our aim is to help the network to better extract features and further improve the performance of remote sensing image retrieval through the sample generation mechanism and intra-class ranking loss. Finally, we conduct extensive experiments on multiple remote-sensing image datasets using multiple evaluation metrics such as mAP@K, which demonstrate that using the sample-generated intra-class ranking loss function can effectively improve the performance of remote sensing image retrieval.

Agricultural Total Water Consumption Coefficient and Its Spatial Correlation Network in Yangtze River Economic Belt

Agriculture contributes extensively to the economic development of countries; however, it is one of the main water-consuming industries. Revealing the characteristics and network structure of agricultural water use efficiency (AWUE) is conducive to green and coordinated development of agriculture. Considering that analyzing the variation of AWUE is helpful to calculating the AWUE, this study aims to calculate the total water consumption coefficient of the agricultural sector in the Yangtze River Economic Belt (YEB) by using the China interregional input-output tables in 2012 and 2017. The gravity model was modified to deduce the spatial correlation network of agricultural total water consumption coefficient (ATWCC), and the social network analysis method was used to analyze the network structural characteristics. The results show that: (1) compared to 2012, the AWUE of YEB in 2017 improved, with a decrease of ATWCC from 532.5 to 387.5 m3/10,000-yuan, account for 27.2%; (2) The network relevance of ATWCC of YEB’s 11 provinces (cities) enhanced, the rank relationship within the network and the network structure was relatively stable; (3) The spatial correlation network formed several network centers, Zhejiang and Jiangsu in the eastern coastal area were the main destinations of the spatial spillover of the spatial correlation network.

Representation of the inferred relationships in a map-like space.

A cognitive map is an internal representation of the external world that guides flexible behavior in a complex environment. Cognitive map theory assumes that relationships between entities can be organized using Euclidean-based coordinates. Previous studies revealed that cognitive map theory can also be generalized to inferences about abstract spaces, such as social spaces. However, it is still unclear whether humans can construct a cognitive map by combining relational knowledge between discrete entities with multiple abstract dimensions in nonsocial spaces. Here we asked subjects to learn to navigate a novel object space defined by two feature dimensions, price and abstraction. The subjects first learned the rank relationships between objects in each feature dimension and then completed a transitive inferences task. We recorded brain activity using functional magnetic resonance imaging (fMRI) while they performed the transitive inference task. By analyzing the behavioral data, we found that the Euclidean distance between objects had a significant effect on response time (RT). The longer the one-dimensional rank distance and two-dimensional (2D) Euclidean distance between objects the shorter the RT. The task-fMRI data were analyzed using both univariate analysis and representational similarity analysis. We found that the hippocampus, entorhinal cortex, and medial orbitofrontal cortex were able to represent the Euclidean distance between objects in 2D space. Our findings suggest that relationship inferences between discrete objects can be made in a 2D nonsocial space and that the neural basis of this inference is related to cognitive maps.

Diabetes Self-Management and Distress Levels in Patients With Diabetes Mellitus: a Cross Sectional Study

Background: Patients suffer Diabetes Mellitus face challenges related care management disease, treatment proper self. Distress condition covers passionate stretch, enthusiastic and behavioral challenges consequence burden management self. Objective: The aim of the think about to analyze the relationship between Diabetes Self-Management and trouble Levels in Diabetes Mellitus patients. Methods: The study used analytic correlation through a cross-sectional approach. Diabetes self-management was variable free and trouble level was variable subordinate. Taking a test of 55 patients utilizing the purposive examining strategy method at Wound Center Madura with criteria inclusion, DM type 1 and 2 patients were undergoing care wounds with ulcer diabetes. Distress instruments used Diabetes Distress Scale and Diabetes Self-Management survey. This ponder utilized the Spearmen rank relationship test. Results: The measurable test comes about appear p-value = 0.000 a = 0.05. showing that there is a connection between diabetes self-management with distressed diabetes patients at Wound Center Madura, Indonesia. Nearly half of diabetes patients have a high level of distress, high emotional burden, indicating a high personal emotional burden in the patient, such as feeling tired in dealing with various disease management. This personal reaction can lead to feelings of fear, anger because of diabetes mellitus, while the behavior of checking blood sugar independently with diabetes self management control.Conclusion: The goal of Diabetes Self-Management is to be able to reduce the Distress and control diabetes mellitus, while poor Diabetes Self-management controls a risk of complications and tends to feel worried and frustrated because they feel unable to control diabetes.

시가화 유형과 PM10 미세먼지 관계에 대한 탐색적 분석 - 경기도 14개 시 사례를 중심으로 -

The purpose of this study is to explore the ranking relationship between developed area types and PM10 concentration. The study site is 14 local governments in Gyeonggi-do, including Gwacheon, Gwangmyeong, Gwangju, Guri, Gunpo, Namyangju, Seongnam, Suwon, Anyang, Osan, Yongin, Uiwang, and Hanam.
 Morphological Spatial Pattern Analysis (MSPA) is performed to understand the morphological spatial pattern of the developed area. Each factor derived as a type of developed area is comparatively analyzed to PM10 concentration by using Spearman Correlation Analysis.
 The main result is that the rank correlation value between the PM10 concentration and the morphological spatial pattern of developed area is very low or almost non-existent in a highly urbanized area. It implies that it might be difficult to observe a particular relationship between the urbanization rate, morphological patterns, and PM10 concentration in highly urbanized areas. It shows that the universal relationship with PM10 concentration can vary depending on the degree of urbanization of the study site.

The Association Between the Bared-Teeth Display and Social Dominance in Captive Chimpanzees (Pan troglodytes)

Humans use smiles — widely observed emotional expressions — in a variety of social situations, of which the meaning varies depending on social relationship and the context in which it is displayed. The homologue of the human smile in non-human primates — both due to morphological and functional similarities — is the bared-teeth display (BT). According to the power asymmetry hypothesis (PAH), species with strict linear dominance hierarchies are predicted to produce distinct communicative signals to avoid escalations of social conflicts. Hence, while the BT in a despotic species is predicted to be expressed from low- to high-ranking individuals, signaling submission, the BT in a tolerant species is predicted to be expressed in multiple contexts, regardless of rank. We tested this hypothesis in a group of 8 captive chimpanzees (Pan troglodytes), a species commonly characterized as rather despotic. An investigation of 11,774 dyadic social interactions revealed this chimpanzee group to have a linear dominance hierarchy, with moderate steepness. A Bayesian GLMM — used to test the effects of social contexts and rank relationships of dyads on the use of the BT display — indicated multi-contextual use of the BT which is contingent on the rank relationship. We also found that slight morphological and/or acoustic variants (i.e., silent bared-teeth and vocalized bared-teeth) of the BT display may have different communicative meanings. Our findings are in line with the prediction derived from the PAH for a moderately despotic species, and the view that the human smile originated from the primate BT display.

Ranking-based scores for the assessment of aesthetic quality in photography

Aesthetic quality assessment in photography has gained popularity in recent years, due to the huge volume of images generated by social networks and the variety of applications this task provides. Main proposals were dependent on the data available to learn from, where datasets are composed by images with ratings from different users. Most of these works reduce the problem to a binary classification [snapshots, professional shots] using rating average.Some articles indicate that the current forms to measure aesthetic quality present important weaknesses, since most of the ratings are concentrated in central values. This paper presents a novel solution by means of Aesthetic Ranking Value (ARV) and Weighted ARV (WARV). Both compute a score value from votes’ distribution able to determine, in an aggregated way, how (aesthetically) good an image is. In this scenario, ARV and WARV show better performance as aesthetic quality scores than the mean because they obtain more distributed and discriminatory information.A study using state-of-the-art frameworks is conducted and a new model based on the ranking relationships is presented. All models are tested on as many tasks as aesthetic scores are available, since each score represents a new ground-truth. Although it is not directly comparable, in this study distinct measurements and statistics support the hypothesis that ARV and WARV are more homogeneously distributed between the extremes. We would like to remark that the change in the ground-truth implied by the ranking-based scores allows perfectly the use of the techniques previously designed for aesthetics quality assessment.

The relationship of maternal rank, 5-HTTLPR genotype, and MAOA-LPR genotype to temperament in infant rhesus monkeys (Macaca mulatta).

Temperament is a construct whose manifestations are quantifiable from an early age, and whose origins have been proposed as "biological." Our goal was to determine whether maternal rank and infant genotype are associated with five measures of temperament in 3- to 4-month old rhesus monkeys (Macaca mulatta), all of whom were born and reared by their mothers in large, outdoor, half-acre cages. Maternal rank was defined as the proportion of animals outranked by each female, and the two genes of interest to us were monoamine oxidase and serotonin transporter, both of which are polymorphic in their promoter regions (MAOA-LPR and 5-HTTLPR, respectively), with one allele of each gene considered a "plasticity" allele, conferring increased sensitivity to environmental events. Our large sample size (n = 2014-3140) enabled us to examine the effects of individual genotypes rather than combining genotypes as is often done. Rank was positively associated with Confident temperament, but only for animals with the 5-repeat allele for MAOA-LPR. Rank had no other effect on temperament. In contrast, genotype had many different effects, with 5-HTTLPR associated with behavioral inhibition, and MAOA-LPR associated with ratings-based measures of temperament. We also examined the joint effect of the two genotypes and found some evidence for a dose-response: animals with the plasticity alleles for both genes were more likely to be behaviorally inhibited. Our results suggest phenotypic differences between animals possessing alleles for MAOA-LPR that show functional equivalence based on in vitro tests, and our data for 5-HTTLPR revealed differences between short/short homozygotes and long/short heterozygotes, strongly suggesting that combining genotypes for statistical analysis should be avoided if possible. Our analysis also provides evidence of sex differences in temperament, and, to our knowledge, the only evidence of differences in temperament based on specific pathogen-freestatus. We suggest several directions for future research.

BIST TEKNOLOJİ İŞLETMELERİNİN NAKİT AKIŞ ORANLARINA DAYALI CRITIC AĞIRLIKLANDIRMALI COCOSO YÖNTEMİ İLE FİNANSAL PERFORMANS ANALİZİ

The aim of the study is to evaluate the financial performance of the companies registered in the BIST Technology index (XUTEK) for the years 2019 and 2020, based on the cash flow rates. In the study, financial performance evaluation of 19 enterprises, whose financial statements for the analysis period registered in XUTEK can be accessed, were carried out. In the performance evaluation, 16 financial ratios based on cash flow ratios calculated from the financial statements published on the Public Disclosure Platform (KAP) were used. The Critic method was used to calculate the criterion weights, and the CoCoSo method, which was developed in recent years from multi-criteria decision making techniques (MCDM), was used in the realization of financial performance rankings. In addition, in order to compare the accuracy of the findings of the CoCoSo method, the ranking results of the two methods were compared by performing the financial performance ranking with the TOPSIS method, which is frequently used in the literature and whose results have been proven to be accurate and reliable. Spearman's Rank Relationship Test was used to measure the consistency between the analysis results. According to the results of the analysis, the financial performance rankings of technology enterprises for the period 2018-2021 have varied over the years. According to the Z value obtained in Spearman's Rank Relationship Test results, it was seen that there was consistency between the methods

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.

A deep learning method and device for bone marrow imaging cell detection.

BackgroundMorphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.MethodsIn this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.ResultsWe verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.ConclusionsClinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

Measuring dominance certainty and assessing its impact on individual and societal health in a nonhuman primate model: a network approach.

The notion of dominance is ubiquitous across the animal kingdom, wherein some species/groups such relationships are strictly hierarchical and others are not. Modern approaches for measuring dominance have emerged in recent years taking advantage of increased computational power. One such technique, named Percolation and Conductance (Perc), uses both direct and indirect information about the flow of dominance relationships to generate hierarchical rank order that makes no assumptions about the linearity of these relationships. It also provides a new metric, known as ‘dominance certainty’, which is a complimentary measure to dominance rank that assesses the degree of ambiguity of rank relationships at the individual, dyadic and group levels. In this focused review, we will (i) describe how Perc measures dominance rank while accounting for both nonlinear hierarchical structure as well as sparsity in data—here we also provide a metric of dominance certainty estimated by Perc, which can be used to compliment the information dominance rank supplies; (ii) summarize a series of studies by our research team reflecting the importance of ‘dominance certainty’ on individual and societal health in large captive rhesus macaque breeding groups; and (iii) provide some concluding remarks and suggestions for future directions for dominance hierarchy research.This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Ranking-Based Deep Hashing Network for Image Retrieval

In large-scale image retrieval, the deep learning-based hashing methods have significantly progressed. However, most of the existing deep hashing methods still have the problems of low feature learning efficiency and weak ranking relationship discrimination. To remedy these problems, a novel Ranking-based Deep Hashing Network (RDHN) is proposed for image retrieval in this paper, which integrates the feature learning module and hash learning module into a unified deep hashing network framework to jointly learn a powerful hash function so that the raw images can be mapped to discrete hash codes with significant discrimination. Specifically, a novel difference convolution is designed based on edge detection operators, and then it is uniquely applied to the first convolutional layer of the convolutional neural network (CNN), which can take advantage of the sensitive characteristics of edge detection operators for edge information to extract richer image edge information. Meanwhile, in hash learning, the ranking metric Mean Average Precision (MAP) is optimized using the idea of scaling, and then a ranking loss function based on MAP is carefully designed to enhance the neighborhood ranking capabilities of the hash codes. Furthermore, to reduce the quantification error, a quantization loss function is also designed. Finally, the ranking loss function is combined with the quantization loss function to form the objective function. The proposed method can generate high-quality discrete hash codes while learning to preserve ranking information, effectively improving retrieval performance. Extensive experimental results on three widely used benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art hashing approaches.

Compressive fatigue response and reliability analysis of thermoplastic composite with low‐velocity impact damage

AbstractThermoplastic composite has attracted more and more attentions for its higher fracture toughness and better damage tolerance than thermosetting composites. AS4/PEEK laminate is a representative thermoplastic composite, which has been widely used in the fabrication of fuselages and aircraft wings. In this paper, low‐velocity impact tests had been carried out on AS4/PEEK laminates. Different impact energy levels were selected for forming impact dent with depth of 1 mm for the three stacking sequences: [0/0/‐45/90/45/0/0/45/90/‐45/0]S, [0/0/45/90/‐45/0/45/90/‐45/0]S, and [45/‐45/0/45/90/‐45/0/45/‐45/0]S, which were 25.5, 21.0, and 20.0 J successively. Then, compression–compression fatigue tests with stress ratio of 10 and frequency of 10 Hz were used to obtain the residual fatigue life and damage propagation. It was found that significant local buckling appeared around the impact dent, which extended perpendicular to the loading direction during the compressive fatigue loading. The increase of the transverse dimension of local buckling could be divided into three stages. The experimental results indicated that a joint Weibull distribution could be used to describe the residual compressive fatigue life of the impacted laminates. This paper assumed an equal rank relationship between the residual strength and fatigue life, and the B‐basis value of fatigue stress was calculated to ensure the reliability of the impacted AS4/PEEK laminates, which were 0.53, 0.50, and 0.53 (normalized by residual compressive stress) at 106 cycles for the three kinds of laminates mentioned earlier.

Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS

Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or more MCDM techniques to address decision-making problems. The authors attempted to create two novel hybrid MCDM systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex PRoportional ASsessment (COPRAS). To demonstrate the ability and effectiveness of these two hybrid models i.e., TOPSIS-ARAS and COPRAS-ARAS were applied to solve a real-time robot selection problem with 12 alternative robots and five selection criteria, while evaluating the parametric importance using the CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. The rankings of the robot alternatives gained from these two hybrid models were also compared to the obtained results from eight other solo MCDM tools. Although the rankings by the applied methods slightly differ from each other, the final outcomes from all of the adopted techniques are consistent enough to suggest that robot 12 is the best choice followed by robot 11, and robot 4 is the worst one among these 12 alternatives. Spearman Correlation Coefficient (SCC) also reveals that the proposed rankings derived from various methods have a strong ranking relationship with one another. Finally, sensitivity analysis was performed to investigate the effects of weight variation and to validate the robustness of the implemented MCDM approaches.

Influence of global rankings on strategic planning from the perspective of decision‐makers: A case study of a Chilean research university

AbstractThis paper complements and extends the literature analysing the relationship of global rankings on universities' strategies, missions, structures, and functions. Specifically, it examines the extent to which global rankings have influenced development strategies of one Chilean university through administrator's perception and changes in the strategic planning. Interview and documentary data were collected at two points in time (2014 and 2017), mainly through semi‐structured interviews with 18 university administrators and college deans. Findings suggest a relationship between the university's ranking position and its perception of academic excellence. However, rankings have effects that are not only perceptual. High ranking positions increase global visibility, promote international partnerships and collaborations that benefit academic staff. At the same time, the university finds itself increasingly bound by the current global standards on academic search, appointment, promotion, and evaluation. Findings suggest that universities are rarely passive recipients of the influence of rankings, they seek opportunities to leverage them to their strategic advantage, more when the ranking benefits their visibility.

Word frequency–rank relationship in tagged texts

We analyze the frequency–rank relationship in sub-vocabularies corresponding to three different grammatical classes (nouns, verbs, and others) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role. Comparing with a null hypothesis which assumes that words belonging to each class are uniformly distributed across the frequency–ranked vocabulary of the whole work, we disclose statistically significant differences between the three classes. This results point to the fact that frequency–rank relationships may reflect linguistic features associated with grammatical function.